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Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

التفاصيل البيبلوغرافية
العنوان: Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer
المؤلفون: Ziyi Wang, Nannan Cao, Jiawei Sun, Heng Zhang, Sai Zhang, Jiangyi Ding, Kai Xie, Liugang Gao, Xinye Ni
المصدر: Radiation Oncology, Vol 19, Iss 1, Pp 1-11 (2024)
بيانات النشر: BMC, 2024.
سنة النشر: 2024
المجموعة: LCC:Medical physics. Medical radiology. Nuclear medicine
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
مصطلحات موضوعية: Breast cancer, Automatic delineation, Cone-beam computed tomography, clinical target volume, Semi-supervised learning, Uncertainty estimation, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
الوصف: Abstract Objectives Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. Methods A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. Results The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89–9.33% and 14.75–16.67%, respectively, while 95HD and ASD decreased by 33.16–67.81% and 36.05–75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. Conclusions Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1748-717X
60854669
Relation: https://doaj.org/toc/1748-717X
DOI: 10.1186/s13014-024-02455-0
URL الوصول: https://doaj.org/article/d66952d898cf4c27ba20b6c608546696
رقم الأكسشن: edsdoj.66952d898cf4c27ba20b6c608546696
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:1748717X
60854669
DOI:10.1186/s13014-024-02455-0